[1]} have worked on the whole face; proposing a new deep neural network architecture to automatically identify facial expressions. Their proposed network consists of two convolutional layers, each followed by max pooling and then four Inception layers. The network is a single-component structure that takes the facial images as an input and classifies them to any of the six basic expressions (anger, disgust, fear, happiness, sadness, and surprise). Their proposed method was tested on seven facial expression databases, namely, DISFA, FERA, SFEW, FER2013, MultiPIE, MMI, and CK. The results of the proposed architecture were better than that of the traditional convolutional neural networks in terms of accuracy and training time.  [2]} proposed a DCNN based facial expression recognition method. The proposed method includes a face detection module assembled from three state-of-the-art face detectors, followed by a classification module assembled from multiple DCNN. Their proposed method was tested on the FER and SFEW data sets. The highest accuracy achieved was \(61.29\%\)  on the SFEW dataset. [3]} have worked on partial facial expression recognition, attempting to find which part of the face (lower, upper, left or right) provides more discriminant information about each facial expression. They found that the lower part of the face (mouth region) provide more information about anger, fear, happiness and sadness, while information about disgust and surprise are well preserved by the upper part of the face (eyes region). In this work, where veiled-faces are used, all parts of the face are occluded, except for the eyes region, from which we attempt to recognize smiles, age and gender, in addition to identifying the person. This makes our work unique, more challenging and important. To the best of our knowledge, this is the first paper to work on veiled-faces solving the four aforementioned problems altogether in one paper, one exception is our previous work [4]}. However, our previous work was based on a smaller veiled-faces database, which does not contain age and smile information in addition to using the traditional hand-crafted features. While this work is based on the use of the deep features and a larger and wealthier veiled-faces database. Table REF  shows how the proposed work is compares from the existing works in terms of various recognition tasks.
